Using inverse methods for estimating soil hydraulic properties from field data as an alternative to direct methods

Ritter, Axel; Hupet, François; Muñoz‐Carpena, Rafael; Lambot, Sébastien; Vanclooster, Marnik

doi:10.1016/s0378-3774(02)00160-9

Cited by 148 publications

(124 citation statements)

References 22 publications

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“…Parameter optimization using a modified-microGA was implemented to identify the soil hydraulic properties as unknown parameters whose variation has large effects on the model outputs [Musters et al, 2000;Hupet et al, 2002;Ritter et al, 2003]. Several major input parameters related to soil moisture dynamics were selected as shown in Table 2 (…”

Section: Soil Parameters Of the Hydrological Modelsmentioning

confidence: 99%

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

2015

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Various hydrological models have been developed for estimating root zone soil moisture dynamics. These models, however, incorporated their own parameterization approaches indicating that the output from the different model inherent structures might include uncertainties because we do not know which model structure is correct for describing the real system. More recently, multimodel approaches using a Bayesian Model Averaging (BMA) scheme can improve the overall predictive skill while individual models retain their own uncertainties for simulated soil moisture based on a single set of weights in modeling under different land surface wetness conditions (e.g., wet, moderately wet, and dry conditions). In order to overcome their limitations, we developed a BMA-based multimodel simulation approach based on various soil wetness conditions for estimating effective surface soil moisture dynamics (0-5 cm) and quantifying uncertainties efficiently based on the land surface wetness conditions. The newly developed approach adapts three different hydrological models (i.e., Noah Land Surface Model, Noah LSM; Soil-Water-Atmosphere-Plant, SWAP; and Community Land Model, CLM) for simulating soil moisture. These models were integrated with a modified-microGA (advanced version of original Genetic Algorithm (GA)) to search for optimized soil parameters for each model. Soil moisture was simulated from the estimated soil parameters using the hydrological models in a forward mode. It was found that SWAP performed better than others during wet condition, while Noah LSM and CLM showed a good agreement with measurements during dry condition. Thus, we inferred that performance of individual models with different model structures can be different with land surface wetness. Taking into account the effects of soil wetness on different model performances, we categorized soil moisture measurements and estimated different weights for each category using the BMA scheme. Effective surface soil moisture dynamics were obtained by aggregating multiple weighted soil moisture. Our findings demonstrated that the effective soil moisture estimates derived by this approach showed a better match with the measurements compared to the original models and single-weighted outputs. Multimodel simulation approach based on land surface wetness enhances the ability to predict reliable soil moisture dynamics and reflects the strengths of different hydrological models under various soil wetness conditions.

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Section: Soil Parameters Of the Hydrological Modelsmentioning

confidence: 99%

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

2015

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“…Inverse solutions based on the Richards equation are now increasingly used for estimating the unsaturated soil hydraulic properties. An advantage of this approach is that the retention and hydraulic conductivity functions can be estimated simultaneously from transient flow data, as shown by many past and recent studies (Abbasi et al, 2003;Abbaspour et al, 1999;Dane and Hruska, 1983;Eching and Hopmans, 1993;Kool and Parker, 1987;Lazarovitch et al, 2007;Minasny and Field, 2005;Ritter et al, 2003;Russo et al, 1991;Schwarzel et al, 2006;Sonnleitner et al, 2003;Zachmann et al, 1981). The soil hydraulic parameters are then obtained by minimizing an objective function, usually defined as the sum of squared deviations between observed and predicted transient water flow variables (Hopmans et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Inverse estimation of soil hydraulic properties under oil palm trees

et al. 2015

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“…When only using 560 generic parameter databases available in HYDRUS, we failed to obtain a parameter set 561 by inverse modeling. Ritter et al (2003) showed that laboratory methods are useful to 562 properly initialize inverse methods for estimating hydraulic parameters, and can be used 563 to reduce the number of fitting parameters. Confidence intervals generally decrease over 564 the course of successive hydraulic parameter estimation and optimization processes.…”

Section: Insert Fig 7 551 Insert Fig 8 552 553mentioning

confidence: 99%

Characterizing hydraulic properties of filter material of a vertical flow constructed wetland

Morvannou¹,

Forquet²,

Vanclooster³

et al. 2013

Ecological Engineering

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10Characterizing the hydraulic properties of filter material used in a vertical flow 11 constructed wetland (VFCW) is a prerequisite to model wastewater treatment using 12process-based models. The filter material is a matrix of porous mineral material and 13 organic matter that makes hydraulic characterization a difficult task. Here, we present a 14 combined laboratory and in situ approach to assess the hydraulic properties of a VFCW 15 installed at Evieu in Ain, France. The laboratory approach produces prior estimates of 16 the local properties of the different VFCW system layers. These prior estimates are 17 subsequently refined with inversely estimated parameters using the HYDRUS-1D code 18 in combination with in situ hydrodynamic measurements. Laboratory experiments 19 consisted of both direct (sand box, pressure chamber, and permeameter experiments) 20 and inv  erse estimates (evaporation method) of hydraulic parameters. In situ 21Abbreviations

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Using inverse methods for estimating soil hydraulic properties from field data as an alternative to direct methods

Cited by 148 publications

References 22 publications

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

Inverse estimation of soil hydraulic properties under oil palm trees

Characterizing hydraulic properties of filter material of a vertical flow constructed wetland

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